OcrV1, PascalFrancis, Corpus, bibRecord, 000A45

Off-line cursive handwriting recognition using hidden Markov models

Identifieur interne : 000A45 ( PascalFrancis/Corpus ); précédent : 000A44; suivant : 000A46

Off-line cursive handwriting recognition using hidden Markov models

Auteurs : H. Bunke ; M. Roth ; E. G. Schukat-Talamazzini

Source :

Pattern recognition [ 0031-3203 ] ; 1995.

RBID : Pascal:95-0540564

Descripteurs français

Pascal (Inist)
- Reconnaissance caractère, Caractère manuscrit, Alphabet, Modèle Markov, Cursive script recognition, Off line recognition, Hidden Markov model, Skeleton graph, OCR.

English descriptors

KwdEn :
- Alphabet, Character recognition, Manuscript character, Markov model, OCR.

Abstract

A method for the off-line recognition of cursive handwriting based on hidden Markov models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An algorithm is applied to the skeleton graph of a word that extracts the edges in a particular order. Given the sequence of edges extracted from the skeleton graph, each edge is transformed into a 10-dimensional feature vector. The features represent information about the location of an edge relative to the four reference lines, its curvature and the degree of the nodes incident to the considered edge. The linear model was adopted as basic HMM topology. Each letter of the alphabet is represented by a linear HMM. Given a dictionary of fixed size, an HMM for each dictionary word is built by sequential concatenation of the HMMs representing the individual letters of the word. Training of the HMMs is done by means of the Baum-Welch algorithm, while the Viterbi algorithm is used for recognition. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A05				`@2 28`
A06				`@2 9`
A08	`01`	`1`	`ENG`	`@1 Off-line cursive handwriting recognition using hidden Markov models`
A11	`01`	`1`		`@1 BUNKE (H.)`
A11	`02`	`1`		`@1 ROTH (M.)`
A11	`03`	`1`		`@1 SCHUKAT-TALAMAZZINI (E. G.)`
A14	`01`			`@1 Univ. Bern, Inst. Informatik angewandte Mathematik @2 3012 Bern @3 CHE @Z 1 aut. @Z 2 aut.`
A20				`@1 1399-1413`
A21				`@1 1995`
A23	`01`			`@0 ENG`
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A61				`@0 A`
A64	`01`	`1`		`@0 Pattern recognition`
A66	`01`			`@0 GBR`
C01	`01`		`ENG`	@0 A method for the off-line recognition of cursive handwriting based on hidden Markov models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An algorithm is applied to the skeleton graph of a word that extracts the edges in a particular order. Given the sequence of edges extracted from the skeleton graph, each edge is transformed into a 10-dimensional feature vector. The features represent information about the location of an edge relative to the four reference lines, its curvature and the degree of the nodes incident to the considered edge. The linear model was adopted as basic HMM topology. Each letter of the alphabet is represented by a linear HMM. Given a dictionary of fixed size, an HMM for each dictionary word is built by sequential concatenation of the HMMs representing the individual letters of the word. Training of the HMMs is done by means of the Baum-Welch algorithm, while the Viterbi algorithm is used for recognition. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each.
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C03	`01`	`X`	`ENG`	`@0 Character recognition @5 01`
C03	`01`	`X`	`SPA`	`@0 Reconocimiento carácter @5 01`
C03	`02`	`X`	`FRE`	`@0 Caractère manuscrit @5 02`
C03	`02`	`X`	`ENG`	`@0 Manuscript character @5 02`
C03	`02`	`X`	`SPA`	`@0 Carácter manuscrito @5 02`
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C03	`06`	`X`	`FRE`	`@0 Off line recognition @4 INC @5 73`
C03	`07`	`X`	`FRE`	`@0 Hidden Markov model @4 INC @5 74`
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C03	`09`	`X`	`FRE`	`@0 OCR @4 CD @5 96`
C03	`09`	`X`	`ENG`	`@0 OCR @4 CD @5 96`
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Format Inist (serveur)

NO :	PASCAL 95-0540564 INIST
ET :	Off-line cursive handwriting recognition using hidden Markov models
AU :	BUNKE (H.); ROTH (M.); SCHUKAT-TALAMAZZINI (E. G.)
AF :	Univ. Bern, Inst. Informatik angewandte Mathematik/3012 Bern/Suisse (1 aut., 2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Pattern recognition; ISSN 0031-3203; Coden PTNRA8; Royaume-Uni; Da. 1995; Vol. 28; No. 9; Pp. 1399-1413; Bibl. 40 ref.
LA :	Anglais
EA :	A method for the off-line recognition of cursive handwriting based on hidden Markov models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An algorithm is applied to the skeleton graph of a word that extracts the edges in a particular order. Given the sequence of edges extracted from the skeleton graph, each edge is transformed into a 10-dimensional feature vector. The features represent information about the location of an edge relative to the four reference lines, its curvature and the degree of the nodes incident to the considered edge. The linear model was adopted as basic HMM topology. Each letter of the alphabet is represented by a linear HMM. Given a dictionary of fixed size, an HMM for each dictionary word is built by sequential concatenation of the HMMs representing the individual letters of the word. Training of the HMMs is done by means of the Baum-Welch algorithm, while the Viterbi algorithm is used for recognition. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each.
CC :	001D02C03
FD :	Reconnaissance caractère; Caractère manuscrit; Alphabet; Modèle Markov; Cursive script recognition; Off line recognition; Hidden Markov model; Skeleton graph; OCR
ED :	Character recognition; Manuscript character; Alphabet; Markov model; OCR
SD :	Reconocimiento carácter; Carácter manuscrito; Alfabeto; Modelo Markov
LO :	INIST-15220.354000054444100070
ID :	95-0540564

Links to Exploration step

Pascal:95-0540564

Le document en format XML

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<EA>A method for the off-line recognition of cursive handwriting based on hidden Markov models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An algorithm is applied to the skeleton graph of a word that extracts the edges in a particular order. Given the sequence of edges extracted from the skeleton graph, each edge is transformed into a 10-dimensional feature vector. The features represent information about the location of an edge relative to the four reference lines, its curvature and the degree of the nodes incident to the considered edge. The linear model was adopted as basic HMM topology. Each letter of the alphabet is represented by a linear HMM. Given a dictionary of fixed size, an HMM for each dictionary word is built by sequential concatenation of the HMMs representing the individual letters of the word. Training of the HMMs is done by means of the Baum-Welch algorithm, while the Viterbi algorithm is used for recognition. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each.</EA>
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Off-line cursive handwriting recognition using hidden Markov models

Off-line cursive handwriting recognition using hidden Markov models

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri